Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin 150081, China.
Medicina (Kaunas). 2023 Jul 11;59(7):1285. doi: 10.3390/medicina59071285.
Triple-negative breast cancer (TNBC), a highly aggressive and heterogeneous subtype of breast cancer, accounts for ap-proximately 10-15% of all breast cancer cases. Currently, there is no effective therapeutic target for TNBC. Tu-mor-associated macrophages (TAMs), which can be phenotypically classified into M1 and M2 subtypes, have been shown to influence the prognosis of various cancers, including ovarian cancer. This study aimed to investigate the role of M1/M2 macrophages in the TNBC tumor microenvironment (TME), with a focus on identifying prognostic genes and predicting immunotherapy response. The study employed the CIBERSORT algorithm to analyze immune cell expression in the TME. Genes associated with the M1/M2 macrophage ratio were identified using Pearson correlation analysis and used to classify patients into dis-tinct clusters. Dimensionality reduction techniques, including univariate Cox regression and Lasso, were applied to these genes. The expression of prognostic genes was validated through immunohistochemistry. The study found a high prevalence of TAMs in the TME. Among the patient clusters, 109 differentially expressed genes (DEGs) were identified. Three significant DEGs (LAMP3, GZMB, and CXCL13) were used to construct the riskScores. The riskScore model effectively stratified patients based on mortality risk. Gene Set Enrichment Analysis (GSEA) associated the riskScore with several significant pathways, including mismatch repair, JAK/STAT3 signaling, VEGF signaling, antigen processing presentation, ERBB signaling, and P53 signaling. The study also predicted patient sensitivity to im-munotherapy using the riskScores. The expression of the three significant DEGs was validated through immunohisto-chemistry. The study concluded that the riskScore model, based on the M1/M2 macrophage ratio, is a valid prognostic tool for TNBC. The findings underscore the importance of the TME in TNBC progression and prognosis and highlight the po-tential of the riskScore model in predicting immunotherapy response in TNBC patients.
三阴性乳腺癌（TNBC）是一种侵袭性和异质性较强的乳腺癌亚型，约占所有乳腺癌病例的 10-15%。目前，TNBC 没有有效的治疗靶点。肿瘤相关巨噬细胞（TAMs）可以表型分类为 M1 和 M2 亚型，已经表明它们可以影响包括卵巢癌在内的各种癌症的预后。本研究旨在探讨 M1/M2 巨噬细胞在 TNBC 肿瘤微环境（TME）中的作用，重点是确定预后基因并预测免疫治疗反应。本研究使用 CIBERSORT 算法分析 TME 中的免疫细胞表达。使用 Pearson 相关性分析确定与 M1/M2 巨噬细胞比例相关的基因，并将患者分为不同的聚类。应用降维技术，包括单变量 Cox 回归和 Lasso，对这些基因进行分析。通过免疫组织化学验证预后基因的表达。研究发现 TAMs 在 TME 中普遍存在。在患者聚类中，鉴定出 109 个差异表达基因（DEGs）。使用三个显著的 DEGs（LAMP3、GZMB 和 CXCL13）构建风险评分。风险评分模型能够有效地根据死亡率对患者进行分层。基因集富集分析（GSEA）将风险评分与几个重要的途径相关联，包括错配修复、JAK/STAT3 信号、VEGF 信号、抗原加工呈递、ERBB 信号和 P53 信号。研究还使用风险评分预测了患者对免疫治疗的敏感性。通过免疫组织化学验证了三个显著 DEGs 的表达。研究得出结论，基于 M1/M2 巨噬细胞比例的风险评分模型是 TNBC 的有效预后工具。这些发现强调了 TME 在 TNBC 进展和预后中的重要性，并突出了风险评分模型在预测 TNBC 患者免疫治疗反应中的潜力。
